Parallel SQL execution in Oracle 10g

Cruanes, Thierry; Dageville, Benoît; Ghosh, Bhaskar

doi:10.1145/1007568.1007666

Cited by 15 publications

(4 citation statements)

References 7 publications

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“…So it is important to pick the most efficient way to redistribute data. Oracle supports several redistribution techniques from which the optimizer decides based on the table sizes, the join method, the join type, the number of processes performing the join, and the hardware configuration [6]. One such technique, referred to as partition-wise join [24], applies when joining two tables on their partitioning key.…”

Section: Parallel Join Processingmentioning

confidence: 99%

“…During the parallel execution of the statement, every SQL operation in the execution plan is divided into tasks, each assigned to a different process (slave process). For example, the scan operation is parallelized by assigning a granule [6] of the scanned table (sequence of database blocks) to a different process. Data produced by a set of processes executing one operation are distributed to the set of processes executing the next operation in the plan.…”

Section: Parallel Executionmentioning

confidence: 99%

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Query optimization in Oracle 12c database in-memory

et al. 2015

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Traditional on-disk row major tables have been the dominant storage mechanism in relational databases for decades. Over the last decade, however, with explosive growth in data volume and demand for faster analytics, has come the recognition that a different data representation is needed. There is widespread agreement that in-memory column-oriented databases are best suited to meet the realities of this new world. Oracle 12c Database In-memory, the industry's first dual-format database, allows existing row major on-disk tables to have complementary in-memory columnar representations. The new storage format brings new data processing techniques and query execution algorithms and thus new challenges for the query optimizer. Execution plans that are optimal for one format may be sub-optimal for the other. In this paper, we describe the changes made in the query optimizer to generate execution plans optimized for the specific formatrow major or columnarthat will be scanned during query execution. With enhancements in several areasstatistics, cost model, query transformation, access path and join optimization, parallelism, and cluster-awarenessthe query optimizer plays a significant role in unlocking the full promise and performance of Oracle Database In-Memory. This section provides a brief introduction to Oracle DBIM; more details are in [13] and [18]. Oracle 12c Database In-Memory (DBIM) is a dual-format database where data from a table can reside in both columnar format in an in-memory column store and in row major format on disk. The in-memory columnar format speeds up analytic queries and the row major format is well-suited for answering OLTP queries. Note that scanning on-disk tables does not necessarily mean disk I/O; some or all of the blocks of the table may be cached in the row major buffer cache [4]. A dedicated in-memory column store called the In-Memory Area acts as the storage for columnar data. The in-memory area is a subset of the database shared global area (SGA).

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Section: Parallel Join Processingmentioning

confidence: 99%

Section: Parallel Executionmentioning

confidence: 99%

Query optimization in Oracle 12c database in-memory

et al. 2015

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“…These queries look similar to standard database operations. For a sufficiently large set of symbolic features, it is likely that feature querying could be approached using existing work in parallelizing SQL queries [33]. However, the feature set the NS-DR extracts is relatively small, meaning that the overhead which parallelism incurs would almost certainly overwhelm the speedup for this model.…”

Section: E Ns-dr Symbolic Program Executormentioning

confidence: 99%

Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterization

Susskind¹,

Arden²,

John³

et al. 2021

Preprint

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Neuro-symbolic artificial intelligence is a novel area of AI research which seeks to combine traditional rules-based AI approaches with modern deep learning techniques. Neurosymbolic models have already demonstrated the capability to outperform state-of-the-art deep learning models in domains such as image and video reasoning. They have also been shown to obtain high accuracy with significantly less training data than traditional models. Due to the recency of the field's emergence and relative sparsity of published results, the performance characteristics of these models are not well understood. In this paper, we describe and analyze the performance characteristics of three recent neuro-symbolic models. We find that symbolic models have less potential parallelism than traditional neural models due to complex control flow and low-operational-intensity operations, such as scalar multiplication and tensor addition. However, the neural aspect of computation dominates the symbolic part in cases where they are clearly separable. We also find that data movement poses a potential bottleneck, as it does in many ML workloads.

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“…Parallel databases [12] such as DB2 [4], Gamma [11], Volcano [14], and Oracle [9] all support preaggregation techniques for SQL base types and built-in aggregators. Some systems such as Oracle also support preaggregation for user-defined functions.…”

Section: Parallel and Distributed Databasesmentioning

confidence: 99%

Distributed aggregation for data-parallel computing

Gunda

Isard

2009

Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles

130

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Data-intensive applications are increasingly designed to execute on large computing clusters. Grouped aggregation is a core primitive of many distributed programming models, and it is often the most efficient available mechanism for computations such as matrix multiplication and graph traversal. Such algorithms typically require non-standard aggregations that are more sophisticated than traditional built-in database functions such as Sum and Max. As a result, the ease of programming user-defined aggregations, and the efficiency of their implementation, is of great current interest.This paper evaluates the interfaces and implementations for user-defined aggregation in several state of the art distributed computing systems: Hadoop, databases such as Oracle Parallel Server, and DryadLINQ. We show that: the degree of language integration between user-defined functions and the high-level query language has an impact on code legibility and simplicity; the choice of programming interface has a material effect on the performance of computations; some execution plans perform better than others on average; and that in order to get good performance on a variety of workloads a system must be able to select between execution plans depending on the computation. The interface and execution plan described in the MapReduce paper, and implemented by Hadoop, are found to be among the worst-performing choices.

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Parallel SQL execution in Oracle 10g

Cited by 15 publications

References 7 publications

Query optimization in Oracle 12c database in-memory

Query optimization in Oracle 12c database in-memory

Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterization

Distributed aggregation for data-parallel computing

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